Quantum Image Processing: Opportunities and Challenges

Yue Ruan,Yuanxia Shen,Xiling Xue,Wenjie Liu

doi:10.1155/2021/6671613

Yue Ruan, Yuanxia Shen + Show 2 more

Open Access

https://doi.org/10.1155/2021/6671613

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Abstract

Quantum image processing (QIP) is a research branch of quantum information and quantum computing. It studies how to take advantage of quantum mechanics’ properties to represent images in a quantum computer and then, based on that image format, implement various image operations. Due to the quantum parallel computing derived from quantum state superposition and entanglement, QIP has natural advantages over classical image processing. But some related works misuse the notion of quantum superiority and mislead the research of QIP, which leads to a big controversy. In this paper, after describing this field’s research status, we list and analyze the doubts about QIP and argue “quantum image classification and recognition” would be the most significant opportunity to exhibit the real quantum superiority. We present the reasons for this judgment and dwell on the challenges for this opportunity in the era of NISQ (Noisy Intermediate-Scale Quantum).

Highlights

Since the concept of quantum computing was proposed by Feynman in 1982 [1], the achievements by many geniuses have shown that quantum computing has dramatically improved computational efficiency. e theory to implement quantum computing is nearly mature; the challenge of realizing universal quantum computing mainly comes from technical issues, such as manipulating large-scale qubits [2]
Whether the method is superior to classical image recognition is unknown, but it does show the great potential of quantum image recognition
Compared with classical image processing, quantum image processing is far from sufficient in both depth and width. e “quantum advantage” claimed in some related published papers has been doubted by many scholars; the core of these doubts is “how to obtain quantum image operating results efficiently and accurately.”

Summary

Introduction

Since the concept of quantum computing was proposed by Feynman in 1982 [1], the achievements by many geniuses have shown that quantum computing has dramatically improved computational efficiency. e theory to implement quantum computing is nearly mature; the challenge of realizing universal quantum computing mainly comes from technical issues, such as manipulating large-scale qubits [2]. In the last format Real Ket, as the pixels’ grayscale information is stored in the probability amplitude of the components of a superposition quantum state, the storage space only need 2n qubits. If taking into account the cost of quantum image preparation and the cost of obtaining the image manipulation result by measurements, the claim that exponential acceleration of quantum image operation may not exist Mastriani enumerated these doubts [42] (similar challenges in quantum machine learning [43]) and concluded that many published works related to QIP are “Quantum Hoax.”. To our knowledge, there seems to be no module in MATLAB that can simulate quantum noise. erefore, if one wants to verify quantum algorithms’ performance on real quantum computing devices, IBM’s qiskit or other tools would be a better choice

Opportunities and Challenges

Conclusions